Comprehensive Study on Zero-Shot Text Classification Using Category Mapping
Existing zero-shot text classification methods based on large pre-trained models with added prompts exhibit strong representational capacity and scalability but have relatively poor commercial applicability. Approaches that fine-tune smaller models using label mappings and existing datasets for zero...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10870154/ |
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| Summary: | Existing zero-shot text classification methods based on large pre-trained models with added prompts exhibit strong representational capacity and scalability but have relatively poor commercial applicability. Approaches that fine-tune smaller models using label mappings and existing datasets for zero-shot classification are simpler but suffer from weaker generalization capabilities. This paper employs three strategies to improve the accuracy and generalization of pre-trained models in zero-shot text classification tasks: 1) Utilizing a pre-trained model that transforms inputs into a standardized multiple-choice format. 2) Constructing a text classification training set using Wikipedia text data to fine-tune the pre-trained model; 3) Proposing a zero-shot category mapping method based on GloVe text similarity, using Wikipedia categories as substitutes for text labels. Without fine-tuning on the target labels, this method achieves performance comparable to the best models fine-tuned with target labels. |
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| ISSN: | 2169-3536 |